--------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\kab235\Dropbox\CovidPopulism\Submi
> ssion PSRM\BaldwinMares_DataReplication\Covidrisk_manu
> script_replication.log
  log type:  text
 opened on:  20 Jul 2022, 13:10:40

. 
. global esttabformat b(%8.2f) se(%8.2f) obs r2(%8.2f) s
> tar(+ 0.10 * 0.05 ** 0.01)

. 
. use "data/Covidrisk_wave1.dta", replace

. 
. 
. ****************************************************
. *FIGURE 2 statistics (graph created using R script)*
. ****************************************************
. 
. *summarize constructed Covid risk variable (statistics
>  reported in text)
. sum knowCovid

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
   knowCovid |      1,996    .2993487    .3609297       
>    0          1

. 
. *statistics reported in figure 2
. sum selfCovid_dummy familyCovid_dummy friendCovid_dumm
> y coworkerCovid_dummy noCovid_dummy

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
selfCovid_~y |      1,996    .1402806    .3473649       
>    0          1
familyCovi~y |      1,996    .1758517    .3807893       
>    0          1
friendCovi~y |      1,996     .259519    .4384806       
>    0          1
coworkerCo~y |      1,996    .0721443    .2587915       
>    0          1
noCovid_du~y |      1,996    .5415832    .4983927       
>    0          1

. 
. ****************************************************
. *FIGURE 3 statistics (graph created using R script)*
. ****************************************************
. 
. *summarize constructed employment risk variable (stati
> stics reported in text)
. sum jobeffectCovid3

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
jobeffectC~3 |      1,996    .2565463    .3611262       
>    0   3.082017

. 
. *statistics reported in figure 3
. sum selfrestoredhrs_dummy selfreducedhrs_dummy selfres
> toredlayoff_dummy selftemplayoff_dummy selflayoff_dumm
> y familyrestoredhrs_dummy familyreducedhrs_dummy famil
> yrestoredlayoff_dummy familytemplayoff_dummy familylay
> off_dummy noeconshock_dummy

    Variable |        Obs        Mean    Std. Dev.      
>  Min        Max
-------------+------------------------------------------
> ---------------
selfrestor.. |      1,996    .0971944    .2962965       
>    0          1
selfreduce~y |      1,996    .1998998     .400025       
>    0          1
selfrestor.. |      1,996    .0430862     .203102       
>    0          1
selftempla~y |      1,996    .0616232    .2405303       
>    0          1
selflayoff~y |      1,996    .0450902    .2075539       
>    0          1
-------------+------------------------------------------
> ---------------
familyrest.. |      1,996    .1147295     .318775       
>    0          1
familyredu~y |      1,996    .1643287    .3706664       
>    0          1
familyrest.. |      1,996    .0666333    .2494483       
>    0          1
familytemp~y |      1,996    .0791583    .2700534       
>    0          1
familylayo~y |      1,996    .0571142    .2321189       
>    0          1
-------------+------------------------------------------
> ---------------
noeconshoc~y |      1,996     .510521    .5000146       
>    0          1

. 
. *********************************
. *FIGURE 4: SUBJECTIVE INSECURITY*
. *********************************
. 
. use "data/Covidrisk_wave2.dta", replace

. 
. *Calculate differences between treatments*
. *tech vs. anti-foreign
. ttest risk if treatment>0 & treatment<3, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |     676    2.704882    .0378983    .9853549  
>   2.630469                                            
>               2.779294
Anti-For |     717    2.792887    .0368995    .9880527  
>   2.720443                                            
>               2.865331
---------+----------------------------------------------
> ----------------------
combined |   1,393    2.750179    .0264548    .9873708  
>   2.698284                                            
>               2.802075
---------+----------------------------------------------
> ----------------------
    diff |           -.0880054     .052899              
>  -.1917758                                            
>                .015765
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-For)              
>           t =  -1.6636
Ho: diff = 0                                     degrees
>  of freedom =     1391

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.0482         Pr(|T| > |t|) = 0.0964      
>     Pr(T > t) = 0.9518

. *anti-foreign versus anti-elite
. ttest risk if treatment>1, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Anti-For |     717    2.792887    .0368995    .9880527  
>   2.720443                                            
>               2.865331
Anti-Eli |     713    2.692146    .0370584    .9895351  
>   2.619389                                            
>               2.764903
---------+----------------------------------------------
> ----------------------
combined |   1,430    2.742657    .0261727    .9897296  
>   2.691316                                            
>               2.793998
---------+----------------------------------------------
> ----------------------
    diff |            .1007412     .052296              
>  -.0018441                                            
>               .2033264
--------------------------------------------------------
> ----------------------
    diff = mean(Anti-For) - mean(Anti-Eli)              
>           t =   1.9264
Ho: diff = 0                                     degrees
>  of freedom =     1428

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.9729         Pr(|T| > |t|) = 0.0543      
>     Pr(T > t) = 0.0271

. *tech versus anti-elite
. ttest risk if treatment!=0 & treatment!=2, by(treatmen
> t)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |     676    2.704882    .0378983    .9853549  
>   2.630469                                            
>               2.779294
Anti-Eli |     713    2.692146    .0370584    .9895351  
>   2.619389                                            
>               2.764903
---------+----------------------------------------------
> ----------------------
combined |   1,389    2.698344    .0264874    .9871677  
>   2.646384                                            
>               2.750304
---------+----------------------------------------------
> ----------------------
    diff |            .0127358    .0530117              
>  -.0912559                                            
>               .1167275
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-Eli)              
>           t =   0.2402
Ho: diff = 0                                     degrees
>  of freedom =     1387

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.5949         Pr(|T| > |t|) = 0.8102      
>     Pr(T > t) = 0.4051

. 
. set scheme plotplain

. 
. preserve

. 
. collapse (mean) meanrisk= risk (sd) sdrisk=risk (count
> ) n=risk, by(treatment)

. 
. generate hirisk = meanrisk + invttail(n-1,0.025)*(sdri
> sk / sqrt(n))

. generate lowrisk = meanrisk - invttail(n-1,0.025)*(sdr
> isk / sqrt(n))

. 
. *Enhanced Bar Graph*
. graph twoway (bar meanrisk treatment if treatment==0) 
> (bar meanrisk treatment if treatment==1) ///
>  (bar meanrisk treatment if treatment==2) (bar meanris
> k treatment if treatment==3) (rcap hirisk lowrisk trea
> tment) /// 
>  (scatteri 2.9 1 2.9 2,  recast(line) lw(medthick)  mc
> (none) lc(black) lp(solid)) ///
>  (scatteri 2.9 1 2.9 2,  recast(dropline) base(2.88) l
> w(medthick) mc(none) lc(black) lp(solid)) ///
>  (scatteri 2.93 2 2.93 3,  recast(line) lw(medthick)  
> mc(none) lc(black) lp(solid)) ///
>  (scatteri 2.93 2 2.93 3,  recast(dropline) base(2.91)
>  lw(medthick) mc(none) lc(black) lp(solid)),  ///
>   text(2.91 1.5 "{&Delta} = 0.088 (SE=0.053)" , size(s
> mall)) text(2.94 2.5 "{&Delta} = 0.101 (SE=0.052)" , s
> ize(small)) ytitle("Average Subjective Insecurity") xl
> abel(0 "Control" 1 "Tech" 2 "Anti-Foreign" 3 "Anti-Eli
> te", noticks) xtitle("") legend( order(1 "Control" 2 "
> Technocratic" 3 "Anti-Foreign" 4 "Anti-Elite") )

. 
. graph export "figures_tables/Figure4.tif", replace
(file figures_tables/Figure4.tif written in TIFF format)

.            
. restore

. 
. ******************************************************
> *****************
. *FIGURE 5: AVERAGE SUPPORT FOR REDISTRIBUTION BY HEALT
> H SHOCK EXPOSURE*
. ******************************************************
> *****************
. 
. use "data/Covidrisk_wave1.dta", replace

. 
. *Tabulation of health shock categorical variable (stat
> istics reported in text)
. tab healthshock

     Health |
      Shock |
(Categorica |
         l) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,081       54.16       54.16
          1 |        635       31.81       85.97
          2 |        280       14.03      100.00
------------+-----------------------------------
      Total |      1,996      100.00

. 
. *Calculate differences between treatments for those wi
> th highest risk exposure*
. *tech vs. anti-foreign
. ttest pandemicsupport if treatment>0 & treatment<3 & h
> ealthshock==2, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |      73    3.640411    .1445834    1.235321  
>   3.352189                                            
>               3.928633
Anti-For |      89    3.837079    .1155866    1.090442  
>   3.607374                                            
>               4.066783
---------+----------------------------------------------
> ----------------------
combined |     162    3.748457    .0910162    1.158448  
>   3.568717                                            
>               3.928196
---------+----------------------------------------------
> ----------------------
    diff |           -.1966677    .1828377              
>  -.5577542                                            
>               .1644188
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-For)              
>           t =  -1.0756
Ho: diff = 0                                     degrees
>  of freedom =      160

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.1419         Pr(|T| > |t|) = 0.2837      
>     Pr(T > t) = 0.8581

. *anti-foreign versus anti-elite
. ttest pandemicsupport if treatment>1 & healthshock==2,
>  by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Anti-For |      89    3.837079    .1155866    1.090442  
>   3.607374                                            
>               4.066783
Anti-Eli |      78    3.323718    .1454943    1.284971  
>   3.034002                                            
>               3.613434
---------+----------------------------------------------
> ----------------------
combined |     167    3.597305     .093573     1.20923  
>   3.412559                                            
>               3.782052
---------+----------------------------------------------
> ----------------------
    diff |            .5133607    .1838266              
>    .150405                                            
>               .8763164
--------------------------------------------------------
> ----------------------
    diff = mean(Anti-For) - mean(Anti-Eli)              
>           t =   2.7926
Ho: diff = 0                                     degrees
>  of freedom =      165

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.9971         Pr(|T| > |t|) = 0.0058      
>     Pr(T > t) = 0.0029

. *tech vs. anti-elite
. ttest pandemicsupport if treatment!=0 & treatment!=2 &
>  healthshock==2, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |      73    3.640411    .1445834    1.235321  
>   3.352189                                            
>               3.928633
Anti-Eli |      78    3.323718    .1454943    1.284971  
>   3.034002                                            
>               3.613434
---------+----------------------------------------------
> ----------------------
combined |     151    3.476821    .1031071    1.267001  
>   3.273091                                            
>               3.680551
---------+----------------------------------------------
> ----------------------
    diff |             .316693    .2053864              
>  -.0891532                                            
>               .7225393
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-Eli)              
>           t =   1.5419
Ho: diff = 0                                     degrees
>  of freedom =      149

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.9374         Pr(|T| > |t|) = 0.1252      
>     Pr(T > t) = 0.0626

. 
. preserve

. 
. collapse (mean) meansupport= pandemicsupport (sd) sdsu
> pport=pandemicsupport (count) n=pandemicsupport, by(he
> althshock treatment)

. 
. generate hisupport = meansupport + invttail(n-1,0.025)
> *(sdsupport / sqrt(n))

. generate lowsupport = meansupport - invttail(n-1,0.025
> )*(sdsupport / sqrt(n))

. 
. gen healthtreatment = treatment+1 if healthshock==0
(8 missing values generated)

. replace healthtreatment = treatment+6 if healthshock==
> 1
(4 real changes made)

. replace healthtreatment = treatment+11 if healthshock=
> =2
(4 real changes made)

. 
. twoway (bar meansupport healthtreatment if treatment==
> 0) ///
>        (bar meansupport healthtreatment if treatment==
> 1) ///
>        (bar meansupport healthtreatment if treatment==
> 2) ///
>        (bar meansupport healthtreatment if treatment==
> 3) ///
>        (rcap hisupport lowsupport healthtreatment), //
> /
>            yscale(range(1 4)) ylabel(1 (1) 4) ///
>            xlabel( 2.5 "Low" 7.5 "Middle" 12.5 "High",
>  noticks) ///
>            xtitle("Health Shock") ytitle("Mean Support
>  Redistribution") ///
>        legend( order(1 "Control" 2 "Technocratic" 3 "A
> nti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/Figure5.tif", replace
(file figures_tables/Figure5.tif written in TIFF format)

.            
. restore

. 
. ******************************************************
> *****************
. *FIGURE 6: AVERAGE SUPPORT FOR REDISTRIBUTION BY ECONO
> MIC SHOCK EXPOSURE*
. ******************************************************
> *****************
. 
. *Find natural break in economic shock amongst responde
> nts exposed to a shock*
. sort jobeffectCovid3

. group1d jobeffectCovid3 if jobeffectCovid3>=0.01, max(
> 2) gen(g2=2)

  Partitions of 974 data up to 2 groups

  1 group:  sum of squares 122.35
  Group Size    First            Last           Mean    
>   SD
   1     974    1  .204905     974  3.08202     0.53    
> 0.35

  2 groups: sum of squares 51.53
  Group Size    First            Last           Mean    
>   SD
   2     103  872  .876401     974  3.08202     1.31    
> 0.52
   1     871    1  .204905     871  .861436     0.43    
> 0.16
   
  Groups     Sums of squares
     1          122.35
     2           51.53

. 
. *Tabulation of employment shock categorical variable (
> statistics reported in text)
. tab econshock

   Economic |
      Shock |
(Categorica |
 l, Natural |
     Break) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,022       51.20       51.20
          1 |        871       43.64       94.84
          2 |        103        5.16      100.00
------------+-----------------------------------
      Total |      1,996      100.00

. 
. *Calculate differences between treatments for those wi
> th highest risk exposure*
. *tech vs. anti-foreign
. ttest pandemicsupport if treatment>0 & treatment<3 & e
> conshock==2, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |      33    3.840909    .2250363    1.292735  
>   3.382525                                            
>               4.299293
Anti-For |      31    3.943548    .1938953    1.079563  
>   3.547561                                            
>               4.339535
---------+----------------------------------------------
> ----------------------
combined |      64    3.890625    .1482448    1.185959  
>   3.594381                                            
>               4.186869
---------+----------------------------------------------
> ----------------------
    diff |           -.1026393    .2987329              
>  -.6997979                                            
>               .4945193
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-For)              
>           t =  -0.3436
Ho: diff = 0                                     degrees
>  of freedom =       62

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.3662         Pr(|T| > |t|) = 0.7323      
>     Pr(T > t) = 0.6338

. *anti-foreign versus anti-elite
. ttest pandemicsupport if treatment>1 & econshock==2, b
> y(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Anti-For |      31    3.943548    .1938953    1.079563  
>   3.547561                                            
>               4.339535
Anti-Eli |      22    3.420455    .2334259    1.094865  
>   2.935019                                            
>                3.90589
---------+----------------------------------------------
> ----------------------
combined |      53    3.726415    .1519802    1.106432  
>   3.421445                                            
>               4.031386
---------+----------------------------------------------
> ----------------------
    diff |            .5230938    .3027133              
>  -.0846285                                            
>               1.130816
--------------------------------------------------------
> ----------------------
    diff = mean(Anti-For) - mean(Anti-Eli)              
>           t =   1.7280
Ho: diff = 0                                     degrees
>  of freedom =       51

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.9550         Pr(|T| > |t|) = 0.0900      
>     Pr(T > t) = 0.0450

. *tech vs. anti-elite
. ttest pandemicsupport if treatment!=0 & treatment!=2 &
>  econshock==2, by(treatment)

Two-sample t test with equal variances
--------------------------------------------------------
> ----------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev. 
>   [95% Conf. Interval]
---------+----------------------------------------------
> ----------------------
Technocr |      33    3.840909    .2250363    1.292735  
>   3.382525                                            
>               4.299293
Anti-Eli |      22    3.420455    .2334259    1.094865  
>   2.935019                                            
>                3.90589
---------+----------------------------------------------
> ----------------------
combined |      55    3.672727    .1651283    1.224625  
>   3.341665                                            
>                4.00379
---------+----------------------------------------------
> ----------------------
    diff |            .4204545    .3352942              
>  -.2520604                                            
>                1.09297
--------------------------------------------------------
> ----------------------
    diff = mean(Technocr) - mean(Anti-Eli)              
>           t =   1.2540
Ho: diff = 0                                     degrees
>  of freedom =       53

    Ha: diff < 0                 Ha: diff != 0          
>        Ha: diff > 0
 Pr(T < t) = 0.8923         Pr(|T| > |t|) = 0.2153      
>     Pr(T > t) = 0.1077

. 
. preserve

. 
. collapse (mean) meansupport= pandemicsupport (sd) sdsu
> pport=pandemicsupport (count) n=pandemicsupport, by(ec
> onshock treatment)

. 
. generate hisupport = meansupport + invttail(n-1,0.025)
> *(sdsupport / sqrt(n))

. generate lowsupport = meansupport - invttail(n-1,0.025
> )*(sdsupport / sqrt(n))

. 
. gen econtreatment = treatment+1 if econshock==0
(8 missing values generated)

. replace econtreatment = treatment+6 if econshock==1
(4 real changes made)

. replace econtreatment = treatment+11 if econshock==2
(4 real changes made)

. 
. twoway (bar meansupport econtreatment if treatment==0)
>  ///
>        (bar meansupport econtreatment if treatment==1)
>  ///
>        (bar meansupport econtreatment if treatment==2)
>  ///
>        (bar meansupport econtreatment if treatment==3)
>  ///
>        (rcap hisupport lowsupport econtreatment), ///
>            yscale(range(1 4)) ylabel(1 (1) 4) ///
>            xlabel( 2.5 "Low" 7.5 "Middle" 12.5 "High",
>  noticks) ///
>            xtitle("Economic Shock") ytitle("Mean Suppo
> rt Redistribution") ///
>        legend( order(1 "Control" 2 "Technocratic" 3 "A
> nti-Foreign" 4 "Anti-Elite") )

. graph export "figures_tables/Figure6.tif", replace
(file figures_tables/Figure6.tif written in TIFF format)

.            
. restore

. 
. 
. *********** 
. **TABLE 3**
. *********** 
.  
. eststo m1a: reg pandemicsupport tech for elite knowCov
> id, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       2.16
                                                Prob > F
>           =     0.0717
                                                R-square
> d         =     0.0046
                                                Root MSE
>           =     1.0318

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0957515   .0763028     1.25   0.210   
>  -.0538903                                            
>               .2453932
         for |   .0197984   .0744613     0.27   0.790   
>  -.1262318                                            
>               .1658287
       elite |  -.0348075   .0757591    -0.46   0.646   
>  -.1833831                                            
>               .1137681
   knowCovid |   .1371069   .0707811     1.94   0.053   
>   -.001706                                            
>               .2759198
       _cons |   3.415682   .0641879    53.21   0.000   
>     3.2898                                            
>               3.541565
--------------------------------------------------------
> ----------------------

. eststo m1b: reg pandemicsupport tech for elite techXkn
> ow forXknow eliteXknow knowCovid, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       3.71
                                                Prob > F
>           =     0.0005
                                                R-square
> d         =     0.0143
                                                Root MSE
>           =     1.0275

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |  -.0808916   .0994386    -0.81   0.416   
>  -.2759064                                            
>               .1141232
         for |  -.2286811    .096303    -2.37   0.018   
>  -.4175466                                            
>              -.0398156
       elite |  -.1286073   .0974308    -1.32   0.187   
>  -.3196846                                            
>               .0624699
   techXknow |    .583444   .2392944     2.44   0.015   
>   .1141498                                            
>               1.052738
    forXknow |   .8194773   .2287054     3.58   0.000   
>   .3709498                                            
>               1.268005
  eliteXknow |   .3072133   .2384915     1.29   0.198   
>  -.1605063                                            
>               .7749329
   knowCovid |  -.3563784   .1966672    -1.81   0.070   
>  -.7420739                                            
>               .0293171
       _cons |   3.566195   .0804452    44.33   0.000   
>    3.40843                                            
>               3.723961
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (elite + eliteXknow)

 ( 1)  for - elite + forXknow - eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .4121903   .1433795     2.87   0.004   
>   .1310003                                            
>               .6933803
--------------------------------------------------------
> ----------------------

. lincom (for + forXknow) - (tech + techXknow)

 ( 1)  - tech + for - techXknow + forXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .0882439   .1423647     0.62   0.535   
>  -.1909558                                            
>               .3674436
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXknow) - (tech + techXknow)

 ( 1)  - tech + elite - techXknow + eliteXknow = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.3239464   .1537353    -2.11   0.035   
>  -.6254457                                            
>              -.0224471
--------------------------------------------------------
> ----------------------

. eststo m1c: reg pandemicsupport tech for elite jobeffe
> ctCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(4, 199
> 0)        =       2.85
                                                Prob > F
>           =     0.0228
                                                R-square
> d         =     0.0077
                                                Root MSE
>           =     1.0301

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0954583    .076115     1.25   0.210   
>  -.0538152                                            
>               .2447318
         for |   .0229561    .074185     0.31   0.757   
>  -.1225323                                            
>               .1684445
       elite |  -.0334719   .0754769    -0.44   0.657   
>   -.181494                                            
>               .1145502
jobeffectC~3 |   .2102836     .08038     2.62   0.009   
>   .0526459                                            
>               .3679214
       _cons |   3.401562   .0637871    53.33   0.000   
>   3.276465                                            
>               3.526658
--------------------------------------------------------
> ----------------------

. eststo m1d: reg pandemicsupport tech for elite techXjo
> beffectCovid3 forXjobeffectCovid3 eliteXjobeffectCovid
> 3 jobeffectCovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.70
                                                Prob > F
>           =     0.0088
                                                R-square
> d         =     0.0103
                                                Root MSE
>           =     1.0296

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
        tech |   .0498857   .0945387     0.53   0.598   
>  -.1355197                                            
>               .2352911
         for |  -.0807613   .0934722    -0.86   0.388   
>  -.2640751                                            
>               .1025524
       elite |   -.056804   .0966073    -0.59   0.557   
>  -.2462662                                            
>               .1326582
techXjobef~3 |   .1711233   .2470405     0.69   0.489   
>  -.3133623                                            
>               .6556089
forXjobeff~3 |   .4052546    .245146     1.65   0.098   
>  -.0755156                                            
>               .8860247
eliteXjobe~3 |   .0853906   .2621695     0.33   0.745   
>  -.4287654                                            
>               .5995466
jobeffectC~3 |   .0328085    .209266     0.16   0.875   
>  -.3775953                                            
>               .4432123
       _cons |   3.448772   .0789998    43.66   0.000   
>   3.293841                                            
>               3.603704
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (elite + eliteXjo
> beffectCovid3)

 ( 1)  for - elite + forXjobeffectCovid3 -
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .2959067   .1686064     1.76   0.079   
>  -.0347572                                            
>               .6265706
--------------------------------------------------------
> ----------------------

. lincom (for + forXjobeffectCovid3) - (tech + techXjobe
> ffectCovid3)

 ( 1)  - tech + for - techXjobeffectCovid3 +
       forXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |   .1034842   .1557031     0.66   0.506   
>  -.2018744                                            
>               .4088428
--------------------------------------------------------
> ----------------------

. lincom (elite + eliteXjobeffectCovid3) - (tech + techX
> jobeffectCovid3)

 ( 1)  - tech + elite - techXjobeffectCovid3 +
       eliteXjobeffectCovid3 = 0

--------------------------------------------------------
> ----------------------
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
         (1) |  -.1924225   .1721933    -1.12   0.264   
>  -.5301209                                            
>               .1452759
--------------------------------------------------------
> ----------------------

. 
. esttab m1a m1b m1c m1d using "figures_tables/Poptable3
> .rtf", order(for elite tech knowCovid forXknow eliteXk
> now techXknow jobeffectCovid3 forXjobeffectCovid3 elit
> eXjobeffectCovid3 techXjobeffectCovid3) $esttabformat 
> replace label onecell
(output written to figures_tables/Poptable3.rtf)

. *Note: lincom estimates added manually to table*
. 
. ******************************************************
> ************************
. *FIGURE 7*
. *DIFFERENCE IN THE EFFECTS OF POPULIST MESSAGES BY HEA
> LTH AND ECONOMIC SHOCKS*
. ******************************************************
> ************************
. 
. set scheme tufte

. 
. reg pandemicsupport knowCovid for elite tech forXknow 
> eliteXknow techXknow, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       3.71
                                                Prob > F
>           =     0.0005
                                                R-square
> d         =     0.0143
                                                Root MSE
>           =     1.0275

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
   knowCovid |  -.3563784   .1966672    -1.81   0.070   
>  -.7420739                                            
>               .0293171
         for |  -.2286811    .096303    -2.37   0.018   
>  -.4175466                                            
>              -.0398156
       elite |  -.1286073   .0974308    -1.32   0.187   
>  -.3196846                                            
>               .0624699
        tech |  -.0808916   .0994386    -0.81   0.416   
>  -.2759064                                            
>               .1141232
    forXknow |   .8194773   .2287054     3.58   0.000   
>   .3709498                                            
>               1.268005
  eliteXknow |   .3072133   .2384915     1.29   0.198   
>  -.1605063                                            
>               .7749329
   techXknow |    .583444   .2392944     2.44   0.015   
>   .1141498                                            
>               1.052738
       _cons |   3.566195   .0804452    44.33   0.000   
>    3.40843                                            
>               3.723961
--------------------------------------------------------
> ----------------------

. 
. predictnl diff_1 = (_b[for] + _b[forXknow]*knowCovid) 
> - (_b[elite] + _b[eliteXknow]*knowCovid), se(diff1_se)

. 
. gen upper1 = diff_1 + diff1_se*1.96

. gen lower1 = diff_1 - diff1_se*1.96

. 
. twoway (line diff_1 upper1 lower1 knowCovid, sort lcol
> or(gs2) lpattern(solid dash dash)), yline(0, lcolor(gs
> 2) lpattern(dot)) legend(position(5) ring(0)) xtitle("
> Exposure to Health Shock", color(gs2)) ///
>        ytitle("Differential Effect of Anti-Foreign vs.
>  Anti-Elite", color(gs2)) ylabel(-0.5 (0.5) 2,nogrid) 
> yscale(range(-0.5 (0.5) 2)) ///
>            xlabel (0 (0.5) 1) graphregion(fcolor(white
> ) ifcolor(white) color(white) lcolor(white) ilcolor(wh
> ite)) ///
>            legend(order(1 "Effect" 2 "95 % CIs"))

. graph export "figures_tables/Figure7_leftpanel.tif", r
> eplace
(file figures_tables/Figure7_leftpanel.tif written in TI
> FF format)

. 
. reg pandemicsupport jobeffectCovid3 for elite tech for
> XjobeffectCovid3 eliteXjobeffectCovid3 techXjobeffectC
> ovid3, robust

Linear regression                               Number o
> f obs     =      1,995
                                                F(7, 198
> 7)        =       2.70
                                                Prob > F
>           =     0.0088
                                                R-square
> d         =     0.0103
                                                Root MSE
>           =     1.0296

--------------------------------------------------------
> ----------------------
             |               Robust
pandemicsu~t |      Coef.   Std. Err.      t    P>|t|   
>   [95% Con                                            
>           f. Interval]
-------------+------------------------------------------
> ----------------------
jobeffectC~3 |   .0328085    .209266     0.16   0.875   
>  -.3775953                                            
>               .4432123
         for |  -.0807613   .0934722    -0.86   0.388   
>  -.2640751                                            
>               .1025524
       elite |   -.056804   .0966073    -0.59   0.557   
>  -.2462662                                            
>               .1326582
        tech |   .0498857   .0945387     0.53   0.598   
>  -.1355197                                            
>               .2352911
forXjobeff~3 |   .4052546    .245146     1.65   0.098   
>  -.0755156                                            
>               .8860247
eliteXjobe~3 |   .0853906   .2621695     0.33   0.745   
>  -.4287654                                            
>               .5995466
techXjobef~3 |   .1711233   .2470405     0.69   0.489   
>  -.3133623                                            
>               .6556089
       _cons |   3.448772   .0789998    43.66   0.000   
>   3.293841                                            
>               3.603704
--------------------------------------------------------
> ----------------------

. 
. predictnl diff_2 =  (_b[for] +_b[forXjobeffectCovid3]*
> jobeffectCovid3) - (_b[elite]+ _b[eliteXjobeffectCovid
> 3]*jobeffectCovid3), se(diff2_se)

. 
. gen upper2 = diff_2 + diff2_se*1.96

. gen lower2 = diff_2 - diff2_se*1.96

. 
. twoway (line diff_2 upper2 lower2 jobeffectCovid3, sor
> t lcolor(gs2) lpattern(solid dash dash)), yline(0, lco
> lor(gs2) lpattern(dot)) legend(position(5) ring(0)) xt
> itle("Exposure to Economic Shock", color(gs2)) ///
>        ytitle("Differential Effect of Anti-Foreign vs.
>  Anti-Elite", color(gs2)) ylabel(-0.5 (0.5) 2,nogrid) 
> yscale(range(-0.5 (0.5) 2)) ///
>            graphregion(fcolor(white) ifcolor(white) co
> lor(white) lcolor(white) ilcolor(white)) ///
>            legend(order(1 "Effect" 2 "95 % CIs"))

. 
. graph export "figures_tables/Figure7_rightpanel.tif", 
> replace
(file figures_tables/Figure7_rightpanel.tif written in T
> IFF format)

.            
. log close
      name:  <unnamed>
       log:  C:\Users\kab235\Dropbox\CovidPopulism\Submi
> ssion PSRM\BaldwinMares_DataReplication\Covidrisk_manu
> script_replication.log
  log type:  text
 closed on:  20 Jul 2022, 13:10:49
--------------------------------------------------------
